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SamYatesSmith

tru8-mcp

by SamYatesSmith
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    The three tools have distinct purposes: check submits a claim, get_result returns analytics, and get_result_raw returns raw data. The two retrieval tools are very similar but explicitly differentiated by descriptions, causing minimal ambiguity.

    Naming Consistency5/5

    All tools follow the pattern tru8_<verb>_<modifier> with consistent snake_case. The verbs 'check' and 'get' are clear, and modifiers 'result' and 'result_raw' are descriptive and uniformly applied.

    Tool Count5/5

    Three tools is well-suited for the narrow domain of fact-checking: one for submitting a claim and two for retrieving results in different formats. No unnecessary tools, and the count is sufficient for the core workflow.

    Completeness4/5

    The tool surface covers the primary workflow (submit and retrieve results with or without analytics). However, there is no tool to list or manage previous checks, which is a minor gap for users who need to track multiple checks.

  • Average 4.6/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the burden. It describes the return value in detail but does not explicitly state that the operation is read-only or non-destructive. However, the term 'retrieve' implies reading, and no side effects are mentioned. A bit more explicitness could improve confidence.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise and well-structured. It starts with a clear purpose sentence, then describes the return value, then gives usage guidance, and finally lists the argument. No unnecessary words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has an output schema (though not shown), the description explains what the output contains in detail (computed block, distributions, etc.). It covers the single parameter well and provides context for when to use the tool. It feels complete for a retrieval tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    There is only one parameter, check_id. The schema has type 'string', and the description adds semantic meaning by stating it is a UUID returned by tru8_check. This helps the agent understand the parameter's source and format.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool retrieves a previously submitted check with pre-computed analytics, using specific verb and resource. It also distinguishes itself from sibling tool tru8_get_result_raw by emphasizing structured analytics.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicit guidance is provided: use this over tru8_get_result_raw when you want structured analytics for summarisation or comparison. This directly helps the agent decide which tool to use.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description fully discloses behavioral traits: auto-detection of URLs, tier fallback with costs and timing, caching behavior (max_age_hours), and output structure. It is transparent about what happens during execution without needing annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with sections for tiers and output, and it is front-loaded with the purpose. It is slightly verbose due to detailed tier and output explanations, but every sentence adds value; minor redundancy is acceptable for clarity.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity, lack of annotations, and 0% schema coverage, the description covers all necessary aspects: input, behavior, parameters, output structure (including nested objects), and execution details. It is fully complete without relying on the output schema.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It thoroughly explains each parameter: claim (text or URL, auto-detection), max_tier (options and default), max_age_hours (purpose), and compact (stripping evidence arrays). This adds significant meaning beyond the bare schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose as evidence research for a factual claim or article URL, using a specific verb and resource. It distinguishes itself from sibling tools (tru8_get_result, tru8_get_result_raw) which are for retrieving results, thus avoiding ambiguity.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explains when to use the tool (for claims or URLs), details the tier system and fallback order, and mentions max_tier for cost control. It provides clear context but does not explicitly state when not to use it or list alternative tools, though the sibling differentiation is implicit.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Discloses no computed block, smaller payload, and lists returned components, though could mention idempotency or side effects.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Concise with summary, returned fields, usage note, and parameter description; no wasted words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Covers purpose, usage, parameter, and expected return types; output schema exists so no need to detail return values further.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description adds that check_id is a UUID from tru8_check, adding meaning beyond the schema's type string.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool retrieves a check result without computed analytics, distinguishing it from tru8_get_result which likely includes computed data.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly advises use when computing own aggregations or needing only specific raw fields, providing clear alternative context.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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